Briefly talk about closures in Python

Closures in Python

Someone left a message a few days ago, and it is unclear about the use of one of the closures and re.sub. I searched on Script Home and found that I had never written anything related to closures, so I decided to summarize and improve the content of Python.

1. The concept of closure

First of all, we have to start with the basic concept. What is closure? Let’s take a look at the explanation on Wiki:

Copy code The code is as follows:

In computer science, closure (Closure) is the abbreviation of lexical closure (Lexical Closure), which is a function that references free variables. The referenced free variable will remain with the function even after it has left the environment in which it was created. Therefore, there is another way of saying that a closure is an entity composed of a function and its associated reference environment. Closures can have multiple instances at runtime, and different reference environments and the same function combination can produce different instances.
....

Two key points are mentioned above: free variables and functions. These two keys will be discussed later. I still have to elaborate on the meaning of "closure". As the text makes clear, it can be understood vividly as a closed package. This package is a function. Of course, there is also the corresponding logic inside the function. What is inside the package is freedom. Variables, free variables can wander around with the package. Of course, there must be a premise that this package is created.

Introducing it through the Python language, a closure means that you call a function A, and this function A returns a function B to you. This returned function B is called a closure. The parameters you pass when calling function A are free variables.

For example:

def func(name):
 def inner_func(age):
  print 'name:', name, 'age:', age
 return inner_func

bb = func('the5fire')
bb(26) # >>> name: the5fire age: 26

When func is called here, a closure - inner_func is generated, and the closure holds the free variable - name, so this also means that when the life cycle of function func ends, the variable name still exists. Because it is referenced by the closure, it will not be recycled.

In addition, closure is not a unique concept in Python. All languages ​​that treat functions as first-class citizens have the concept of closure. However, closures can also be used in languages ​​like Java where classes are first-class citizens, but they must be implemented using classes or interfaces.

For more conceptual things, please refer to the reference link at the end.

2. Why use closures

Based on the above introduction, I wonder if readers feel that this thing is somewhat similar to classes. The similarity is that they both provide data encapsulation. The difference is that the closure itself is a method. Just like classes, we often abstract common things into classes when programming (of course, as well as modeling the real world-business) to reuse common functions. The same is true for closures. When we need function-granular abstraction, closures are a good choice.

At this point, a closure can be understood as a read-only object. You can pass a property to it, but it can only provide you with an execution interface. Therefore, in programs we often need such a function object - closure, to help us complete a common function, such as the decorator which will be mentioned later.

3. Use closures

The first scenario, a very important and common usage scenario in Python is the decorator. Python provides a very friendly "syntax sugar" for the decorator - @, which allows us to use the decorator very conveniently. I won’t elaborate too much on the principle of decoration. In short, if you add @decorator_func to a function func, it is equivalent to decorator_func(func):

def decorator_func(func):
 def wrapper(*args, **kwargs):
  return func(*args, **kwargs)
 return wrapper

@decorator_func
def func(name):
 print 'my name is', name

# 等价于
decorator_func(func)

In this example of the decorator, the closure (wrapper) holds the external func parameter and can accept external parameters. The accepted parameters are passed to func intact and the execution result is returned.

This is a simple example. If it is slightly more complicated, you can have multiple closures, such as the frequently used LRUCache decorator. The decorator can accept parameters like @lru_cache(expire=500). The implementation is the nesting of two closures:

def lru_cache(expire=5):
 # 默认5s超时
 def func_wrapper(func):
  def inner(*args, **kwargs):
   # cache 处理 bala bala bala
   return func(*args, **kwargs)
  return inner
 return func_wrapper

@lru_cache(expire=10*60)
def get(request, pk)
 # 省略具体代码
 return response()

Students who don’t know much about closures must be able to understand the above code. This is an interview question we often asked in previous interviews.

The second scenario is based on a feature of closure-"lazy evaluation". This application is more common when accessing the database, for example:

# 伪代码示意

class QuerySet(object):
 def __init__(self, sql):
  self.sql = sql
  self.db = Mysql.connect().corsor() # 伪代码

 def __call__(self):
  return db.execute(self.sql)

def query(sql):
 return QuerySet(sql)

result = query("select name from user_app")
if time > now:
 print result # 这时才执行数据库访问

The above inappropriate example shows the function of lazy evaluation through closure, but the result returned by the above query is not a function, but a class with functional functions. If you are interested, you can take a look at the implementation of Django's queryset. The principle is similar.

The third scenario is the situation where the parameters of a certain function need to be assigned values ​​in advance. Of course, there is already a good solution in Python to access functools.parial, but it can also be achieved using closures.

def partial(**outer_kwargs):
 def wrapper(func):
  def inner(*args, **kwargs):
   for k, v in outer_kwargs.items():
    kwargs[k] = v
   return func(*args, **kwargs)
  return inner
 return wrapper

@partial(age=15)
def say(name=None, age=None):
 print name, age

say(name="the5fire")
# 当然用functools比这个简单多了
# 只需要： functools.partial(say, age=15)(name='the5fire')

It seems like this is another far-fetched example, but it can be regarded as a practical application of closures.

Finally, to summarize, closures are easy to understand and are widely used in Python. This article is a summary of closures. If you have any questions, please leave a message.

4. References

Wikipedia - Closures

http://stackoverflow.com/questions/4020419/closures-in-python

http://www.shutupandship.com/2012/01/python-closures-explained.html

http://stackoverflow.com/questions/141642/what-limitations-have-closures-in-python-compared-to-language-x-closures

http://mrevelle.blogspot.com/2006/10/closure-on-closures.html